Evals - AI Agent Evaluation Framework Skill

Customization

Before executing, check for user customizations at: ~/.opencode/skills/PAI/USER/SKILLCUSTOMIZATIONS/Evals/

If this directory exists, load and apply any PREFERENCES.md, configurations, or resources found there. These override default behavior. If the directory does not exist, proceed with skill defaults.

🚨 MANDATORY: Voice Notification (REQUIRED BEFORE ANY ACTION)

You MUST send this notification BEFORE doing anything else when this skill is invoked.

Send voice notification:

curl -s -X POST http://localhost:8888/notify \
  -H "Content-Type: application/json" \
  -d '{"message": "Running the WORKFLOWNAME workflow in the Evals skill to ACTION"}' \
  > /dev/null 2>&1 &

Output text notification:

Running the **WorkflowName** workflow in the **Evals** skill to ACTION...

This is not optional. Execute this curl command immediately upon skill invocation.

Evals - AI Agent Evaluation Framework

Comprehensive agent evaluation system based on Anthropic's "Demystifying Evals for AI Agents" (Jan 2026).

Key differentiator: Evaluates agent workflows (transcripts, tool calls, multi-turn conversations), not just single outputs.

When to Activate

"run evals", "test this agent", "evaluate", "check quality", "benchmark"
"regression test", "capability test"
Compare agent behaviors across changes
Validate agent workflows before deployment
Verify ALGORITHM ISC rows
Create new evaluation tasks from failures

Core Concepts

Three Grader Types

| Type | Strengths | Weaknesses | Use For | |------|-----------|------------|---------| | Code-based | Fast, cheap, deterministic, reproducible | Brittle, lacks nuance | Tests, state checks, tool verification | | Model-based | Flexible, captures nuance, scalable | Non-deterministic, expensive | Quality rubrics, assertions, comparisons | | Human | Gold standard, handles subjectivity | Expensive, slow | Calibration, spot checks, A/B testing |

Evaluation Types

| Type | Pass Target | Purpose | |------|-------------|---------| | Capability | ~70% | Stretch goals, measuring improvement potential | | Regression | ~99% | Quality gates, detecting backsliding |

Key Metrics

pass@k: Probability of at least 1 success in k trials (measures capability)
pass^k: Probability all k trials succeed (measures consistency/reliability)

Workflow Routing

| Trigger | Workflow | |---------|----------| | "run evals", "evaluate suite" | Run suite via Tools/AlgorithmBridge.ts | | "log failure" | Log failure via Tools/FailureToTask.ts log | | "convert failures" | Convert to tasks via Tools/FailureToTask.ts convert-all | | "create suite" | Create suite via Tools/SuiteManager.ts create | | "check saturation" | Check via Tools/SuiteManager.ts check-saturation |

Quick Reference

CLI Commands

# Run an eval suite
bun run ~/.opencode/skills/Evals/Tools/AlgorithmBridge.ts -s <suite>

# Log a failure for later conversion
bun run ~/.opencode/skills/Evals/Tools/FailureToTask.ts log "description" -c category -s severity

# Convert failures to test tasks
bun run ~/.opencode/skills/Evals/Tools/FailureToTask.ts convert-all

# Manage suites
bun run ~/.opencode/skills/Evals/Tools/SuiteManager.ts create <name> -t capability -d "description"
bun run ~/.opencode/skills/Evals/Tools/SuiteManager.ts list
bun run ~/.opencode/skills/Evals/Tools/SuiteManager.ts check-saturation <name>
bun run ~/.opencode/skills/Evals/Tools/SuiteManager.ts graduate <name>

ALGORITHM Integration

Evals is a verification method for THE ALGORITHM ISC rows:

# Run eval and update ISC row
bun run ~/.opencode/skills/Evals/Tools/AlgorithmBridge.ts -s regression-core -r 3 -u

ISC rows can specify eval verification:

| # | What Ideal Looks Like | Verify |
|---|----------------------|--------|
| 1 | Auth bypass fixed | eval:auth-security |
| 2 | Tests all pass | eval:regression |

Available Graders

Code-Based (Fast, Deterministic)

| Grader | Use Case | |--------|----------| | string_match | Exact substring matching | | regex_match | Pattern matching | | binary_tests | Run test files | | static_analysis | Lint, type-check, security scan | | state_check | Verify system state after execution | | tool_calls | Verify specific tools were called |

Model-Based (Nuanced)

| Grader | Use Case | |--------|----------| | llm_rubric | Score against detailed rubric | | natural_language_assert | Check assertions are true | | pairwise_comparison | Compare to reference with position swap |

Domain Patterns

Pre-configured grader stacks for common agent types:

| Domain | Primary Graders | |--------|-----------------| | coding | binary_tests + static_analysis + tool_calls + llm_rubric | | conversational | llm_rubric + natural_language_assert + state_check | | research | llm_rubric + natural_language_assert + tool_calls | | computer_use | state_check + tool_calls + llm_rubric |

See Data/DomainPatterns.yaml for full configurations.

Task Schema (YAML)

task:
  id: "fix-auth-bypass_1"
  description: "Fix authentication bypass when password is empty"
  type: regression  # or capability
  domain: coding

  graders:
    - type: binary_tests
      required: [test_empty_pw.py]
      weight: 0.30

    - type: tool_calls
      weight: 0.20
      params:
        sequence: [read_file, edit_file, run_tests]

    - type: llm_rubric
      weight: 0.50
      params:
        rubric: prompts/security_review.md

  trials: 3
  pass_threshold: 0.75

Resource Index

| Resource | Purpose | |----------|---------| | Types/index.ts | Core type definitions | | Graders/CodeBased/ | Deterministic graders | | Graders/ModelBased/ | LLM-powered graders | | Tools/TranscriptCapture.ts | Capture agent trajectories | | Tools/TrialRunner.ts | Multi-trial execution with pass@k | | Tools/SuiteManager.ts | Suite management and saturation | | Tools/FailureToTask.ts | Convert failures to test tasks | | Tools/AlgorithmBridge.ts | ALGORITHM integration | | Data/DomainPatterns.yaml | Domain-specific grader configs |

Key Principles (from Anthropic)

Start with 20-50 real failures - Don't overthink, capture what actually broke
Unambiguous tasks - Two experts should reach identical verdicts
Balanced problem sets - Test both "should do" AND "should NOT do"
Grade outputs, not paths - Don't penalize valid creative solutions
Calibrate LLM judges - Against human expert judgment
Check transcripts regularly - Verify graders work correctly
Monitor saturation - Graduate to regression when hitting 95%+
Build infrastructure early - Evals shape how quickly you can adopt new models

ALGORITHM: Evals is a verification method
Science: Evals implements scientific method
Browser: For visual verification graders

Agent Skills: Evals - AI Agent Evaluation Framework

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